

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A			:					List[Any]						=			logging.get_logger(__name__)

__A			:					Dict						=			{
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "swin"

   lowerCamelCase__				=      {
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }





   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			int=224     , __lowerCamelCase					:			int=4     , __lowerCamelCase					:			int=3     , __lowerCamelCase					:			str=96     , __lowerCamelCase					:			Any=[2, 2, 6, 2]     , __lowerCamelCase					:			Tuple=[3, 6, 12, 24]     , __lowerCamelCase					:			Any=7     , __lowerCamelCase					:			Optional[int]=4.0     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			Optional[Any]=0.0     , __lowerCamelCase					:			List[Any]=0.1     , __lowerCamelCase					:			List[Any]="gelu"     , __lowerCamelCase					:			List[Any]=False     , __lowerCamelCase					:			List[Any]=0.02     , __lowerCamelCase					:			Dict=1e-5     , __lowerCamelCase					:			Optional[Any]=32     , __lowerCamelCase					:			List[Any]=None     , __lowerCamelCase					:			List[Any]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   patch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   embed_dim
       SCREAMING_SNAKE_CASE										=   depths
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   num_heads
       SCREAMING_SNAKE_CASE										=   window_size
       SCREAMING_SNAKE_CASE										=   mlp_ratio
       SCREAMING_SNAKE_CASE										=   qkv_bias
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   drop_path_rate
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   use_absolute_embeddings
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   encoder_stride
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE										=   int(embed_dim * 2 ** (len(__lowerCamelCase			) - 1)			)
       SCREAMING_SNAKE_CASE										=   ["stem"] + [f"stage{idx}" for idx in range(1     , len(__lowerCamelCase			) + 1			)]
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   get_aligned_output_features_output_indices(
           out_features=__lowerCamelCase     , out_indices=__lowerCamelCase     , stage_names=self.stage_names			)





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      version.parse("1.11"		)





   @property
   def      _snake_case		(       self					:			Tuple			):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ]			)





   @property
   def      _snake_case		(       self					:			List[str]			):
       return 1e-4






'''simple docstring'''






from __future__ import annotations






def   __A    (				a_    :	float   ,a_    :	float   ,a_    :	float   ,):
		if (electron_conc, hole_conc, intrinsic_conc).count(0						) != 1:
				raise ValueError("You cannot supply more or less than 2 values"						)
		elif electron_conc < 0:
				raise ValueError("Electron concentration cannot be negative in a semiconductor"						)
		elif hole_conc < 0:
				raise ValueError("Hole concentration cannot be negative in a semiconductor"						)
		elif intrinsic_conc < 0:
				raise ValueError(
				    "Intrinsic concentration cannot be negative in a semiconductor"						)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    intrinsic_conc**2 / hole_conc,
				)
		elif hole_conc == 0:
				return (
				    "hole_conc",
				    intrinsic_conc**2 / electron_conc,
				)
		elif intrinsic_conc == 0:
				return (
				    "intrinsic_conc",
				    (electron_conc * hole_conc) ** 0.5,
				)
		else:
				return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



